There is a growing interest in enhancing the security of authentication systems using credentials based on biometric features. Biometric features are singular, meaning that each individual has only one of any given biometric feature. Once the number of authentication applications outstrips the number of biometric features an individual possess, an unavoidable overlap in usage occurs based on the pigeonhole principle. Without overcoming the singular nature of biometric features, a biometrically-enhanced authentication system provides an opportunity and incentive for the permanent compromise of the underlying biometric feature information for authentication purposes.
In one example, an authentication system may use fingerprint biometric. Many fingerprint representation and matching approaches have been devised. These approaches must address the challenge of overcoming inter-user similarities and intra-user variations due to their significant, detrimental impact on authentication performance.
At a low (or local) level, variations between fingerprint samples can include nonlinear distortion between minutiae and detection of spurious minutiae (1102 and 1104 in FIG. 11), which can affect the establishment of relationships between pairs of minutiae. As the security provided by the authentication system heavily depends upon discriminating the relationships between pairs of minutiae, the consistent selection of pairs is critical. Selecting too many pairs can result in the overshadowing of germane fingerprint details, leading to coincidental mismatches of details between fingerprint samples originating from the same or different fingers. Selecting too few pairs can result in the omission of germane fingerprint details, rendering the discrimination process impotent. Therefore, pairs of minutiae between which to establish relationships are selected carefully and consistently. Many approaches exist for selecting pairs of minutiae with the goal of consistently selecting the same pairs of minutiae across fingerprint samples originating from the same finger. Unlike the approach described herein, these existing approaches tend to have fatal flaws stemming from their uses of inflexible structures, difficult parameters, and arbitrary decisions.
FIG. 1A is a schematic that shows neighbor selection using a fixed-radius approach. The fixed-radius approach pairs each minutia with all minutiae surrounding it within a fixed radius. Although unaffected by the presence of spurious minutiae, the inflexible nature of the fixed radius results in sporadic pairing of minutiae in the presence of nonlinear distortion (as shown in FIG. 1A). Additionally, the effective determination of the radius can be difficult. Schematic 102 shows omission of correct neighbor due to relative distortion, schematic 104 shows the original neighborhood, and schematic 106 shows omission of correct neighbor due to spurious minutia.
FIG. 1B is a schematic that shows neighbor selection using a k-nearest neighbors approach. The k-nearest neighbor approach pairs each minutia with the k-nearest minutiae where k is a fixed value. The inflexible, competitive nature of the approach results in sporadic pairing of minutiae in the presence of nonlinear distortion or spurious minutiae (as shown in FIG. 1B). Additionally, the effective determination of k can be difficult. Schematic 108 shows omission of correct neighbor due to relative distortion, schematic 110 shows the original neighborhood, and schematic 112 shows omission of correct neighbor due to spurious minutia.
FIG. 1C is a schematic that shows neighbor selection using a fixed sectors approach. The fixed sectors approach pairs each minutia with the closest minutia from each of S fixed sectors in a round-robin style until it has been paired with k minutiae where S and k are fixed values. The inflexible nature of the fixed sectors and the inflexible, competitive nature of the approach results in sporadic pairing of minutiae in the presence of nonlinear distortion or spurious minutiae as shown in FIG. 1C. Additionally, the effective determination of S and k can be difficult. Schematic 114 shows omission of correct neighbor due to relative distortion, schematic 116 shows the original neighborhood, and schematic 118 shows omission of correct neighbor due to spurious minutia.
FIG. 1D is a schematic that shows neighbor selection using a Voronoi-diagram approach. The Voronoi-diagram approach pairs each minutia with those minutiae with which it shares a border in a Voronoi diagram. The volatile and competitive nature of the approach results in sporadic pairing of minutiae in the presence of nonlinear distortion or spurious minutiae as shown in FIG. 1D. A benefit of the approach is that it does not necessitate the effective determination of any fixed values. Schematic 120 shows omission of correct neighbor due to relative distortion, schematic 122 shows the original neighborhood, and schematic 124 shows omission of correct neighbor due to spurious minutia.
As described previously herein, known approaches to pairing minutiae suffer from conceptual flaws. Accordingly, what is needed, as recognized by the present inventor, is a method and system capable of consistently selecting the same pairs of minutiae across fingerprint samples originating from the same finger, resulting in the authentication system providing greater security through definitive discernment of fingerprint samples originating from different fingers and, conversely, definitive association of fingerprint samples originating from the same finger.
The foregoing “Background” description is for the purpose of generally presenting the context of the disclosure. Work of the inventor, to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present invention.